Sahranavardfard, N.; Aubagnac-Karkar, D.; Costante, G.; Rahantamialisoa, F.N.Z.; Habchi, C.; Battistoni, M. Computation of Real-Fluid Thermophysical Properties Using a Neural Network Approach Implemented in OpenFOAM. Fluids2024, 9, 56.
Sahranavardfard, N.; Aubagnac-Karkar, D.; Costante, G.; Rahantamialisoa, F.N.Z.; Habchi, C.; Battistoni, M. Computation of Real-Fluid Thermophysical Properties Using a Neural Network Approach Implemented in OpenFOAM. Fluids 2024, 9, 56.
Sahranavardfard, N.; Aubagnac-Karkar, D.; Costante, G.; Rahantamialisoa, F.N.Z.; Habchi, C.; Battistoni, M. Computation of Real-Fluid Thermophysical Properties Using a Neural Network Approach Implemented in OpenFOAM. Fluids2024, 9, 56.
Sahranavardfard, N.; Aubagnac-Karkar, D.; Costante, G.; Rahantamialisoa, F.N.Z.; Habchi, C.; Battistoni, M. Computation of Real-Fluid Thermophysical Properties Using a Neural Network Approach Implemented in OpenFOAM. Fluids 2024, 9, 56.
Abstract
Machine learning (ML) based on neural network (NN) facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver, called realFluidReactingNNFoam, under development at University of Perugia in OpenFOAM with NN algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained NN models. Currently, NN models are trained against data generated using the Peng-Robinson equation of state (PR-EoS) assuming mixture frozen temperature. The OpenFOAM solver, where needed, calls the NN models in each grid-cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for the thermophysical properties is achieved via the “Neural Network Inference in C made Easy” (NNICE) library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the PR-EoS and the Chung transport models. First, the development of a real-fluid model (RFM) with artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational ef-ficiency. Results of the overall implementation indicate that the combined OpenFOAM and ML approach provides a speed-up factor higher than seven, while preserving the original solver accuracy.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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